Intro to Data Analysis

Agenda

• Introduction to Data Analysis
• Frequency Distributions
• Lab 01

Readings

1. What’s the point?
2. Being a Statistician Means Never Having to Say You’re Certain

\[ \definecolor{mathBlack}{RGB}{0,0,0} \definecolor{mathOrange}{RGB}{253, 126, 20} \definecolor{mathLightGreen}{RGB}{32, 201, 151} \definecolor{mathGreen}{RGB}{24, 188, 156} \definecolor{mathYellow}{RGB}{253, 156, 18} \definecolor{mathBlue}{RGB}{52, 152, 219} \definecolor{mathRed}{RGB}{231, 76, 60} \definecolor{mathPurple}{RGB}{111, 66, 193} \]

Introduction to Data Analysis

• UNIT OF ANALYSIS
  
• POPULATION
  
• SAMPLE
  
• N & n

• DESCRIPTIVE STATISTICS
  
• INFERENTIAL STATISTICS
  
• TIDY DATA
  
• VARIABLES
  

• NOMINAL
  
• ORDINAL
  
• INTERVAL-RATIO
  
• DICHOTOMOUS
  

Unit of Analysis

Who or what is being studied?

POPULATION

All units of analysis (people, institutions, groups, etc.) in which the researcher is interested.

SAMPLE

A subset of people (or institutions, groups, etc.) selected from a population.

Notation

N refers to population size

n refers to sample size

DESCRIPTIVE STATISTICS

Procedures that help us organize and describe data collected from a sample or population.

INFERENTIAL STATISTICS

Making underlying predictions or inferences about a population using observations and analyses from a sample.

Tidy Data

VARIABLES

Any factor, trait, or condition that can exist in differing amounts or types.

Measurement Levels

Nominal
A variable made up of categories that cannot be ordered

Ordinal
A variable made up of ranked categories, with no systematic or measurable numeric difference between the categories.

Interval-ratio (aka continuous)
A variable with categories that are ordered and expressed in the same units.

Dichotomous (aka binary)
A variable with only two categories.

Which of the following is an example of descriptive statistics?

• The average age of UofT students is 21 years.
• Students who study more than 10 hours a week are more likely to achieve higher grades
• About 65% of U of T undergrads feels socially connected, based on a sample of 300 students.
• A hypothesis test shows a significant relationship between income and education level

Which of the following is an example of a nominal level of measurement?

• The type of religion a person identifies with (e.g., Christianity, Islam, Judaism)
• The number of children in a household
• The income of a family in dollars
• A ranking of provinces by marriage rate.

Which level of measurement is used when ranking neighborhoods by crime rate (e.g., low, medium, high)?

• Ordinal
• Nominal
• Dichotomous
• Continuous

Frequency Distributions

• FREQUENCY DISTRIBUTION
  
• RELATIVE FREQUENCY DISTRIBUTION

• PROPORTION
  
• PERCENTAGE
  
• CUMULATIVE
  
• RATE
  

• BAR GRAPH
  
• HISTOGRAM
  
• LINE GRAPH
  
• STATISTICAL MAP

DISTRIBUTION

Shows all the possible values (or intervals) of the data and how often they occur.

FREQUENCY DISTRIBUTION

A table reporting the number of observations falling into each category of the variable.

gss_all$premarsx <-as_factor(zap_missing(gss_all$premarsx))
gss_all$sex <-as_factor(zap_missing(gss_all$sex))

gss_all %>%
  select(id, year, sex, premarsx) %>%
  filter(year == 2024, !is.na(premarsx)) %>%
  count(premarsx) -> freq_premarsx

freq_premarsx %>%
  summarise(across(where(is.numeric), sum)) %>%
  mutate(premarsx = "Total"
         ) -> total_row

# combine
table_premarsx <- rbind(freq_premarsx, total_row)

Tab A

Table 1. Attitudes about sex before marriage

# Render the table
table_premarsx %>%
  flextable() %>%
  style_flextable()

premarsx	n
always wrong	357
almost always wrong	122
wrong only sometimes	258
not wrong at all	1,378
Total	2,115

Survey question: There’s been a lot of discussion about the way morals and attitudes about sex are changing in this country. If a man and woman have sex relations before marriage, do you think it is _________.

Tab B

Table 1. Attitudes about sex before marriage

table_premarsx %>%
  flextable() %>%
  style_flextable() %>%
  color(color = "#E74C3C", i = 5, j = "n")

premarsx	n
always wrong	357
almost always wrong	122
wrong only sometimes	258
not wrong at all	1,378
Total	2,115

The number of respondents who answered this survey question.

Tab C

Table 1. Attitudes about sex before marriage

table_premarsx %>%
  flextable() %>%
  style_flextable() %>%
  color(color = "#E74C3C", i = 3, j = "n")

premarsx	n
always wrong	357
almost always wrong	122
wrong only sometimes	258
not wrong at all	1,378
Total	2,115

The number of respondents who said pre-marital sex was “wrong only sometimes.”

Source: U.S. General Social Survey 2024

Are women more likely than men to say premarital sex is “not wrong at all”?

Table 2. Attitudes about sex before marriage by gender

gss_all$premarsx <-as_factor(zap_missing(gss_all$premarsx))
gss_all$sex <-as_factor(zap_missing(gss_all$sex))

gss_all %>%
  select(id, year, sex, premarsx) %>%
  filter(!is.na(premarsx), !is.na(sex)) %>%
  group_by(sex) %>%
  count(premarsx) %>%
  pivot_wider(
    names_from = sex,
    values_from = n,
    values_fill = 0
  ) -> freq_premarsx 

# create total row
total_row <- freq_premarsx %>%
  summarise(across(where(is.numeric), sum)) %>%
  mutate(premarsx = "Total") %>%
  select(names(freq_premarsx))  # ensure column order matches

# combine
table_premarsx <- rbind(freq_premarsx, total_row)

# Get number of rows
n_rows <- nrow(table_premarsx) # fixing transparency

# Render the table
table_premarsx %>%
  flextable() %>%
  style_flextable() %>%
  # Manually add zebra stripes with solid colors
  bg(i = seq(1, n_rows, by = 2), bg = "white", part = "body") %>% # fixing transparency
  bg(i = seq(2, n_rows, by = 2), bg = "#F2F2F2", part = "body")

premarsx	male	female
always wrong	4,159	7,116
almost always wrong	1,499	2,388
wrong only sometimes	3,904	4,792
not wrong at all	10,672	11,086
Total	20,234	25,382

Source: U.S. General Social Survey 1972-2024

RELATIVE FREQUENCY DISTRIBUTION

A table showing the proportion or percentage for each value of a variable.

Proportions are between 0 and 1.0.

Proportion = count (f) / total number of cases (N).

Percentages are between 0 and 100.

Percentage = proportion × 100.

gss_all$premarsx <- droplevels(gss_all$premarsx)

# Create frequency & proportions table
tab <- gss_all %>%
  filter(!is.na(premarsx), !is.na(sex)) %>%
  group_by(sex, premarsx) %>%
  summarise(n = n(), .groups = "drop") %>%
  group_by(sex) %>%
  mutate(percent = round(100 * n / sum(n), 0)) %>%
  ungroup() %>%
    pivot_wider(
    names_from = sex, values_from = c(n, percent)) 

# Add totals row
tab_totals <- tab %>%
  summarise(across(where(is.numeric), sum, na.rm = TRUE)) %>%
  mutate(premarsx = "Total")

Warning: There was 1 warning in `summarise()`.
ℹ In argument: `across(where(is.numeric), sum, na.rm = TRUE)`.
Caused by warning:
! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
Supply arguments directly to `.fns` through an anonymous function instead.

  # Previously
  across(a:b, mean, na.rm = TRUE)

  # Now
  across(a:b, \(x) mean(x, na.rm = TRUE))

# Combine with original table
tab_with_totals <- bind_rows(tab, tab_totals)

Tab A

Table 2. Attitudes about sex before marriage by gender

## Pretty table
tab_with_totals %>%
  select(premarsx, n_male, percent_male, n_female, percent_female) %>%
  flextable() %>% 
  style_flextable() %>%
  set_header_labels(
    n_male = "n",    percent_male = "%",
    n_female = "n",  percent_female = "%") %>%
  add_header_row(
    values = c("", "Men", "Women"),
    colwidths = c(1, 2, 2)) %>%
  align(j = c(2, 3, 4, 5), align = "center", part = "all") %>%
  color(color = "#18bc9c", i = 4, j = 4) %>%
  color(color = "#fd7e14", i = 5, j = 4) %>%
  color(color = "#e74c3c", i = 4, j = 5)

	Men		Women
premarsx	n	%	n	%
always wrong	4,159	21	7,116	28
almost always wrong	1,499	7	2,388	9
wrong only sometimes	3,904	19	4,792	19
not wrong at all	10,672	53	11,086	44
Total	20,234	100	25,382	100

\(\frac{\color{mathGreen}{11{,}086}}{\color{mathOrange}{25{,}382}} = 0.4367 \times 100 = \color{mathRed}{43.7\%}\)

TIP: Total of a % column should always sum to 100!

Tab B

Table 2. Attitudes about sex before marriage by gender

## Pretty table
tab_with_totals %>%
  select(premarsx, n_male, percent_male, n_female, percent_female) %>%
  flextable() %>% 
  style_flextable() %>%
  set_header_labels(
    n_male = "n",    percent_male = "%",
    n_female = "n",  percent_female = "%") %>%
  add_header_row(
    values = c("", "Men", "Women"),
    colwidths = c(1, 2, 2)) %>%
  align(j = c(2, 3, 4, 5), align = "center", part = "all") %>%
  color(color = "#E74C3C", i = 4, j = 3) %>%
  color(color = "#E74C3C", i = 4, j = 5)

	Men		Women
premarsx	n	%	n	%
always wrong	4,159	21	7,116	28
almost always wrong	1,499	7	2,388	9
wrong only sometimes	3,904	19	4,792	19
not wrong at all	10,672	53	11,086	44
Total	20,234	100	25,382	100

A greater proportion of men (53%) than women (44%) say premarital sex is “not wrong at all.”

Source: U.S. General Social Survey 1972-2024

CUMULATIVE FREQUENCY DISTRIBUTION

The number or percentage of observations at or below a given category.

Table 3. Attitudes about sex before marriage, with cumulative percentages

gss_all$premarsx <- droplevels(gss_all$premarsx)

# Create frequency & proportions table
tab <- gss_all %>%
  filter(year == 2024, !is.na(premarsx)) %>%
  group_by(premarsx) %>%
  summarise(n = n(), .groups = "drop") %>%
  mutate(percent = round(100 * n / sum(n), 0),
         cum_percent = round(cumsum(percent), 0)) %>%
  ungroup() 

# Add totals row
tab_totals <- tab %>%
  summarise(across(where(is.numeric), sum, na.rm = TRUE)) %>%
  mutate(premarsx = "Total")

# Combine with original table
tab_with_totals <- bind_rows(tab, tab_totals)

## Pretty table
tab_with_totals %>%
  flextable() %>% 
  style_flextable() %>%
  set_header_labels(
    n = "n",    percent = "%",
    cum_percent = "cumulative %") %>%
  color(color = "#18bc9c", i = 1, j = 3) %>%
  color(color = "#fd7e14", i = 2, j = 3) %>%
  color(color = "#e74c3c", i = 2, j = 4)

premarsx	n	%	cumulative %
always wrong	357	17	17
almost always wrong	122	6	23
wrong only sometimes	258	12	35
not wrong at all	1,378	65	100
Total	2,115	100	175

\({\color{mathGreen} 17} + {\color{mathOrange} 6} = {\color{mathRed} 23\%}\)

Lab 01

• CODEBOOK

will add later